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Optimum parameters in a model for tumour control probability, including interpatient heterogeneity: evaluation of the log-normal distribution In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks.
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We found that the variability of temporal parameters of handwriting-handwriting duration and response time-is also well described by a lognormal distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal distribution. Neuronal variability during handwriting: lognormal distribution.ĭirectory of Open Access Journals (Sweden)įull Text Available We examined time-dependent statistical properties of electromyographic (EMG signals recorded from intrinsic hand muscles during handwriting. No sampling method-dependent differences are perceptible for the uniform distribution methods. The importance sampling method gives much smaller sampling uncertainty. Four different methods to sample the multidimensional parameter space with a limited number of samples are investigated: random, stratified, Latin Hypercube sampling with a uniform distribution of parameters and importance sampling using a lognormal distribution that approximates the posterior distribution. The method utilises a fairly large number of pre-determined forward biokinetic calculations, whose results are stored in interpolation tables. Correlations between different parameters are obtained. The distribution is found to be a multivariate log-normal. Using a Bayesian analysis, the joint probability distribution of these six parameters is determined empirically for two cases with quite a lot of bioassay data. Melo, D.Ī simplified biokinetic model for 137 Cs has six parameters representing transfer of material to and from various compartments. International Nuclear Information System (INIS) the identity matrix), but Matlab's explanation of the algorithm,although brief, doesn't suggest this behavior should arise.An empirical multivariate log-normal distribution representing uncertainty of biokinetic parameters for 137Cs I understand that it's easy to make an LHS that isn't space-filling (e.g. The following figures result (first figure is with 1e4 samples so that individual points are visible):Īs is quite visible, there are p block-gaps along the main diagonal (testing with other p confirms this). S=lhsdesign(n,p) %default algorithm maximizes min distance between samples I suppose the fact that the results are SO bad is what makes me doubt that this is Matlab's mistake, though I'm almost certain it is. I'm new to Latin hypercube sampling, and am trying to understand if the somewhat odd sampling that results from the Matlab function lhsdesign is a limitation of the particular algorithm or something deeper within LHS, which I've failed to realize from the literature.